Small and medium-sized enterprises (SMEs) are the backbone of any economy, making up 98 per cent of all businesses in the Asia-Pacific (APAC) region. Factors such as economic shifts, consumer behaviour, government policies, and competitive pressures are causing accelerated adoption of digital technologies, leading to a growing concern over security. Over 84 per cent of companies in APAC are willing to migrate to cloud-based solutions if it ensures greater security.

Zooming into security, traditional on-premises video surveillance systems, while effective, often come with high upfront costs and ongoing maintenance burdens. With the region’s growing demand for flexible, scalable and cost-effective solutions, cloud-based video surveillance systems are quickly becoming the preferred choice.

Here’s why cloud solutions are transforming security for SMEs.

Lower upfront costs, higher long-term value

In APAC, where many SMEs are growing rapidly but still working within limited budgets, the ability to avoid hefty upfront investments is crucial. Cloud-based video surveillance systems allow businesses to bypass the upfront capital expenditures on hardware, infrastructure and installation. Businesses can consider adopting a predictable operating expense model through cloud subscriptions, spreading costs over time.

In the long run, this shift to the cloud also removes the need for continuous maintenance and costly hardware upgrades, as these updates are handled automatically. As such, a reduced financial burden will help SMEs to focus on other parts of the business and growing their operations, while freeing up finances for investing in other strategic initiatives.

Maintenance-free and always up-to-date

The APAC region is known for its fast-paced development in technology, and SMEs here are looking for solutions that require minimal upkeep. Traditional systems, which require regular hardware maintenance and periodic software updates are expensive and time-consuming.

In contrast, cloud systems are managed entirely by the service provider. This means automatic updates and maintenance are included, ensuring access to the latest features and security patches.

Also Read: Why your business should consider a multicultural cybersecurity team

In Hong Kong, for instance, where seamless solutions are in high demand, cloud-based systems provide businesses with peace of mind by ensuring their video surveillance is always updated and secure, without the need for dedicated IT staff.

Moreover, cloud systems offer built-in network security measures and disaster recovery features, reducing the need for businesses to invest separately on cybersecurity solutions.

Seamless scalability as your business grows

As businesses expand, so do their security needs. One of the significant advantages of cloud-based systems is the scalability they offer. SMEs can quickly add new cameras, new locations or new users without the need for complex installations or new hardware.

This is a huge benefit in cities such as Jakarta or Manila, where commercial sectors are rapidly expanding. The flexibility of the cloud allows businesses to adapt quickly to changing demands and ensures that scaling up doesn’t come with expensive IT investments.

Another key benefit is access to advanced features like AI-powered analytics, which can be deployed on-demand without upgrading physical hardware. Cloud-based systems give SMEs the ability to tap into cutting-edge technology that would otherwise be financially out of reach, ensuring companies stay ahead of the curve as they grow.

Hybrid harmony — best of both worlds

For businesses already using on-premises systems, the switch to cloud-based surveillance can seem daunting. However, a hybrid model provides an ‘off-ramp’ from on-premises infrastructure to cloud.

By combining cloud-based features with existing on-premises infrastructure, businesses can migrate gradually and without disruption. It eliminates the need for a complete overhaul of the infrastructure, allowing cameras and other hardware to be upgraded gradually as they reach the end of their lifecycle.

Many SMEs in APAC face strict local regulations (e.g., China’s PIPL, Singapore’s PDPA, or India’s Data Protection Laws) that require keeping certain data on-premises. Hybrid solutions allow businesses to meet these requirements while still leveraging the cloud for operational efficiency.

The hybrid approach also allows integrators to tackle challenges like data integration, bandwidth requirements and security concerns in a phased manner. Working closely with cloud service providers, integrators can also optimise network configurations and implement robust security protocols to ensure seamless integration.

With comprehensive training and support, vendors working closely with their partners will make it easier for end-users to adopt new cloud-based systems while maintaining their current operations.

Advanced security features for peace of mind

Cloud-based video surveillance systems provide a level of security that traditional systems often lack. Advanced features such as end-to-end encryption for data in transit and at rest, multi-factor authentication, and automated security updates ensure that businesses are protected against modern cyber threats. These features are essential for SMEs to protect their data and ensure compliance with increasingly strict data protection regulations.

Also Read: What if cybersecurity included everyone it protects?

In addition, cloud-based systems offer enhanced redundancy and disaster recovery capabilities, keeping critical data safe and accessible even in the event of hardware failure. Comprehensive audit trails and access logs also improve accountability, making it easier for businesses to comply with data protection regulations.

These advanced features provide SMEs with enterprise-level security at a cost that fits their budget.

Instant intel

Real-time situational awareness is crucial for business owners. Cloud-based video surveillance systems provide instant alerts, enabling rapid responses to potential security threats. With advanced AI algorithms, these systems can distinguish between routine events and genuine security risks, reducing false alarms and allowing businesses to focus on verified threats.

By providing real-time insights and proactive alerts, cloud-based systems not only improve security but also optimise resource allocation, allowing SMEs to allocate their time and personnel more efficiently, leading to better business outcomes.

Unlocking the true value: Maximising ROI

For small businesses, the return on investment (ROI) in cloud-based video surveillance is easily measurable. Lower hardware costs, reduced maintenance and enhanced security all contribute to a significant ROI.

Additionally, integrating cloud-based systems with other business operations such as point-of-sale systems or access control helps streamline processes, improve customer service, and allows business owners to make data-driven decisions that contribute to overall business efficiency.

In conclusion, cloud solutions are not just transforming the way SMEs operate; they are fundamentally reshaping how businesses approach security. By offering scalability, cost-efficiency, and advanced protection, cloud technologies enable SMEs to stay agile while safeguarding critical data and assets.

As cyber threats become more complex and regulatory requirements evolve, cloud-based surveillance provides a robust framework that helps SMEs not only mitigate risks but also reach growth milestones faster than their competitors.

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The fashion industry has long struggled with a costly and unsustainable flaw: overproduction. Billions of garments are made each year that never find a home—leading to lost revenue, wasted resources, and overflowing landfills. But what if there was a smarter way to align what brands produce with what customers actually buy?

Enter Nūl, a startup on a mission to fix fashion’s inventory problem using agentic AI. Founded by Malini Kannan and Raghav MS and launched recently by Wavemaker Impact, Nūl helps brands make sharper, faster decisions across their supply chains—reducing overstock, increasing sell-through, and cutting environmental waste along the way.

In this Q&A, Malini and Raghav walk us through how Nūl works, why agentic AI is a game-changer for retail, and what they’ve learned from working at the intersection of technology, sustainability, and fashion.

What inspired you to tackle the issue of overproduction in the fashion industry, and how does Nūl’s mission align with your personal values?

Malini Kannan (MK): At Nūl, we firmly believe that aligning profit with sustainability is possible, even as we drive for significant growth.

A major issue at the heart of this belief is overproduction, which is a common challenge in the fashion industry. When a fashion brand miscalculates, the consequences can be severe—not only affecting profitability but also hindering the ability to fund future collections.

It’s also wasteful, considering the resources, time, and labour involved in producing clothing. The situation becomes even worse when brands are unable to sell their items, even at discounted prices.

These unsold goods often end up in a cycle of being recycled into secondary markets or products. In the worst-case scenario, they are discarded in landfills.

Now imagine that for every 10 items a brand produces, it only sells six or seven at full price. How can we close this gap? This is the problem we aim to solve.

Can you walk us through a typical use case of Nūl’s technology for a medium-sized fashion brand, from data integration to actionable insights?

MK: Let’s take a typical example of a medium-sized fashion brand (with ~US$50 million in annual revenue) that has retail stores in two to three countries and sells online through its own store and a few regional marketplaces.

This firm has six seasons per year and some evergreen staples. Every season, it stocks about 200,000 to 250,000 units of clothes across 20-25 styles, allocated across its different stores and online channels. It is left with anywhere between 30-40 per cent of the inventory unsold each season. Today, it manages its inventory levels through a combination of ERP and Exel spreadsheets.

Also Read: Wavemaker Impact launches Nūl with US$500K investment to tackle fashion overproduction

Nūl ingests data from the ERP and Excel sheets used by various business teams without any change to its existing system. Through our simple web-based platform, we provide teams with real-time sales and inventory levels across all of the brand’s retail, online, and marketplace channels.

Furthermore, it provides smart recommendations on how to reallocate inventory. Assuming an item is selling really well at one location and expected to sell out within the next few days, Nūl will suggest reallocating from a store where the same stock isn’t moving as fast.

It identifies micro-trends around size, style, and colour at specific locations, allowing brands to anticipate and trigger reorders.

In addition, Nūl computes SKU-level performance data in real time and provides longer-term forecasts for planning & production.

Simultaneously, at the SKU level, for non-performing stock, it suggests timely shorter-term actions such as markdowns (minimising inventory holding periods or inventory sent for recycling)

Most importantly, it learns from the actions the brand took and customises its approach to the cycle.

What metrics do you use to measure Nūl’s success, both in terms of business growth and environmental impact?

MK:

Growth Metrics: We are still in our build phase, so right now, we are focused on getting customers to pilot with us to understand the variety of use cases they are using Nūl for in their operations and incorporating these into the core of our solution. More customers, more use cases, more variables – leading to a more robust solution.

Environmental impact: Currently, we are focused on helping brands reduce overproduction. Each apparel that isn’t likely to sell has embedded carbon emissions and water usage that varies based on the type of material, dye and process that went into making it.

A simple cotton t-shirt, say, would have about 6-8kgs of carbon emissions in its production and 2,700 litres of water consumed in the process. We provide brands with a baseline linked to their sell-through and the improvements delivered by using our solution to bring down the full-price unsold apparel’ number.

What are the biggest risks or challenges you foresee for Nūl in the coming years, and how are you preparing to address them?

MK: We are part of a bigger ecosystem of solutions required to truly support the fashion industry’s move toward more sustainable production. Keeping in mind our mission, some of the biggest risk factors would be the speed of development and adoption of a broader range of solutions that can get the industry there.

Specific to Nūl, it would be to build with momentum to capitalise on the global potential of our solution quickly.

Can you explain in simple terms how the term “agentic AI” applies to Nūl’s technology?

Raghav MS (RMS): Agentic AI is like a smart assistant that’s always learning and helping teams make faster, better decisions in real-time—based on what’s happening, not just what happened.

At Nūl, we’ve embedded agentic AI into the core of our inventory optimisation engine. Each agent has a specific role—whether forecasting SKU-level demand, evaluating stock imbalances, or optimising store-to-store transfers. These agents operate independently but coordinate using protocols we’ve built, such as a multi-agent coordination protocol and a model context protocol, to ensure they’re aligned and context-aware.

This allows our system to detect emerging patterns—like a sudden spike in demand for a product in one store—and proactively reallocate stock from slower-moving locations. It transforms inventory management from reactive to autonomous, dramatically reducing overproduction and lost sales.

What data sources does Nūl use to train its AI models, and how do you ensure the quality and relevance of that data?

RMS: Nūl’s AI models are trained on both internal and external data. Internally, we pull from POS and ERP systems like Shopify, SAP, NetSuite, and Oracle—tracking sales velocity, inventory, and sell-through at the SKU-store-week level.

Externally, we layer real-time signals such as weather data, regional holidays, footfall patterns, search trends, and campaign metadata. This is where our Model Context Protocol (MCP) comes in—it ensures every prediction is contextualised based on when, where, and why a trend is happening.

Also Read: A deep-dive into Wavemaker Impact’s decarbonisation strategies in SEA

To maintain accuracy, we use multi-stage validation and real-time feedback loops, allowing agents to self-correct and continuously improve.

How does Nūl’s platform handle the complexity of fashion inventory, with variables like size, style, colour, and location?

RMS: Fashion inventory is inherently multidimensional. A single product can have dozens of variants across size, colour, and style, and each performs differently depending on the store location, customer demographic, and time of year.

Nūl’s platform is architected to operate at the SKU–attribute–store–time level, meaning we don’t treat “a dress” as a single item—we model demand separately for the size M, black variant of that dress in, say, a downtown boutique in Singapore versus a suburban outlet.

Our AI agents understand that a size S beige jumpsuit might sell out quickly in a warm, urban Singapore store catering to younger professionals, while the same SKU in XL, the navy might lag in a different neighbourhood with different customer behaviour.

We integrate real-time POS, ERP, and e-commerce data with contextual signals like weather, foot traffic, and local events. This allows our system to dynamically balance inventory across the network—not just by style or category, but by the exact size and colour that’s needed, where it’s needed.

As a result, Nūl optimises inventory at a level of precision traditional systems can’t match—minimising overstock, maximising availability, and ensuring high sell-through across every variant.

What has been the most surprising or counterintuitive insight you’ve gained from implementing AI in the fashion supply chain?

RMS: One of the most surprising insights has been how micro-level decisions often outperform big seasonal strategies. We used to think the biggest gains would come from improving forecasts at the start of the season. In reality, we saw brands unlocking more value by making small, frequent adjustments during the season—like moving 10 units of a fast-selling SKU from one store to another or tweaking restock timing by a few days.

Another counterintuitive finding? High-performing stores often become overstocked simply because they’re performing well—not because they need more stock. Without AI, brands tend to overcorrect by overfeeding their best stores. Our system shows that sometimes the smarter move is to let a product sell out and redirect those units to where growth potential is higher.

It flipped how we thought about “winning” in fashion retail—it’s less about big bets and more about small, fast moves at the right time.

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Hari Vijayarajan

Hari Vijayarajan, CEO (Asia Pacific) of Singapore-based refurbished electronic devices marketplace Reebelo, has announced his departure after nearly ten years in the region’s startup ecosystem.

Vijayarajan, who joined Reebelo following leadership roles at Lazada and ONE Championship, shared news of his exit via a LinkedIn post last week.

His departure marks the end of a significant chapter in his career, during which he contributed to the growth and regional expansion of several high-growth startups across Southeast Asia and the wider Asia-Pacific region.

“It’s time for me to bid farewell to my incredible team at Reebelo in the coming weeks,” Vijayarajan wrote, reflecting on a decade-long journey that began when he returned to Singapore from the United States in 2015.

Also Read: Circular raises US$7.6M funding for electronic gadgets subscription service

At Reebelo, Vijayarajan played a pivotal role in the company’s growth story, helping the marketplace achieve EBIT profitability while continuing to scale revenue.

Under his leadership, the marketplace expanded its offerings beyond refurbished tech to include categories such as used fashion and refurbished sports gear, with a global footprint spanning Australia, New Zealand, Singapore, Malaysia, and Hong Kong.

In addition to overseeing commercial operations, Vijayarajan contributed across multiple business functions including HR, finance, legal, marketing, operations, and tech—experiences he described as “incredibly rewarding” in the fast-paced environment of a Series A startup.

“I leave with immense pride in the accomplishments we’ve achieved as a team,” he said.

Looking ahead, Vijayarajan shared that he has “no clear plans” for his next move—a first in his 22-year career. “This feels both daunting and liberating at the same time,” he wrote. Over the next few months, he plans to take time off to focus on personal growth, wellness, and family while exploring emerging interests such as artificial intelligence and coaching.

Vijayarajan also looks forward to attending his 15-year reunion at the University of Chicago Booth School of Business later this month.

Reebelo, founded in 2019, has grown into one of Asia-Pacific’s leading marketplaces for refurbished electronics and sustainable lifestyle products with backing from investors including Cathay Innovation, FJ Labs, and Antler.

No announcement has been made yet regarding Vijayarajan’s successor.

(An earlier version of the article mentioned Vijayarajan stepped down as Reebelo’s Chief Commercial Officer. The error is regretted)

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The escalating trade tensions between the United States and China, particularly in light of President Donald Trump’s recent tariff policies is giving me chills. The announcement of these sweeping tariffs, dubbed “Liberation Day” by the Trump administration, has sent shockwaves through financial markets, impacting everything from traditional equities to cryptocurrencies like Bitcoin and Ethereum.

Today, on April 7, 2025, the world is grappling with the fallout of this bold economic move, and I’d like to offer my perspective on how these developments are reshaping the global financial landscape, with a particular focus on their implications for cryptocurrencies and broader market sentiment.

The latest chapter in this saga began when Trump unveiled a comprehensive tariff strategy on April 2, 2025, imposing a 10 per cent baseline levy on all US imports, with steeper duties targeting specific countries—34 per cent on China and 20 per cent on the European Union, among others. This policy, aimed at addressing trade imbalances and bolstering domestic manufacturing, was met with swift retaliation from Beijing, which announced additional 34 per cent tariffs on all US goods just days later.

The tit-for-tat escalation has heightened fears of a full-blown global trade war, pushing investors to seek refuge in safe-haven assets like US Treasury bonds and gold, while riskier assets—stocks, commodities, and cryptocurrencies—have taken a significant hit. The MSCI US index plummeted 6.0 per cent in response, with US equity futures signalling a further 3.3 per cent drop at the open, reflecting the deepening gloom among investors.

For cryptocurrencies, the impact has been particularly pronounced. Bitcoin, the bellwether of the crypto market, has tumbled below US$78,000, trading at US$77,840 as of Sunday—a six per cent decline that mirrors the broader retreat in risk sentiment. This drop comes after a staggering US$247 million in long liquidations rocked the market over a 24-hour period, a clear sign that traders are unwinding their bullish positions amid the uncertainty.

Ethereum, the second-largest cryptocurrency by market cap, has fared even worse, plunging below US$1,600 and erasing over 14 per cent of its value in the same timeframe, with US$217 million in liquidations adding fuel to the fire. These dramatic sell-offs underscore the vulnerability of digital assets to macroeconomic shocks, particularly when investor confidence in traditional markets begins to waver.

What’s striking about this downturn is how it contrasts with the optimism that surrounded cryptocurrencies earlier this year. Bitcoin hit an all-time high of US$109,000 in January, buoyed by Trump’s election victory in November 2024 and his subsequent pro-crypto rhetoric. During his campaign, Trump pivoted from being a crypto skeptic to a vocal supporter, promising to make the US the “crypto capital of the world” and even floating the idea of a national cryptocurrency stockpile.

That enthusiasm carried over into the early months of his administration, with Bitcoin trading above US$80,000 for much of 2025 despite intermittent volatility. Ethereum, too, enjoyed a robust start to the year, hovering above US$1,800 as recently as last week. But the tariff announcement has flipped the script, exposing the fragility of these gains in the face of broader economic headwinds.

Also Read: Global markets reel as Trump tariffs slam stocks and Bitcoin prices

The interplay between Trump’s tariffs and the crypto market is a fascinating case study in how geopolitical and economic policies can ripple through decentralised ecosystems. Historically, Bitcoin has been touted as a hedge against inflation and economic instability—qualities that should, in theory, make it resilient during times like these.

Indeed, some analysts argue that tariffs could ultimately bolster Bitcoin’s long-term appeal by weakening the US dollar’s dominance and driving interest in alternative assets. Jeff Park from Bitwise Asset Management, for instance, suggested that a sustained tariff war could be “amazing for Bitcoin in the long run” due to its potential to undermine traditional currencies. Yet, in the short term, the data tells a different story: Bitcoin and Ethereum are moving in lockstep with risk assets like tech stocks, not as a counterweight to them.

This correlation is evident in the broader market dynamics. The Nasdaq Composite, a tech-heavy index, is careening toward a bear market, while the S&P 500 has shed 4.8 per cent in a single day—its worst drop since June 2020. Defensive sectors like Consumer Staples and Real Estate, while still down, have outperformed the broader market, signalling a flight to safety that hasn’t yet extended to cryptocurrencies.

Meanwhile, commodities like Brent crude have slumped toward US$65 per barrel, reflecting fears that tariffs will dampen global demand growth just as OPEC+ ramps up supply. The US Dollar Index has edged up 0.9 per cent, consolidating recent losses, but Treasury yields are pulling back—the 10-year at 3.99 per cent and the 2-year at 3.65 per cent—as recession odds climb. Gold, typically a rival safe haven to Bitcoin, has held firm above US$3,000 per ounce despite a 2.5 per cent dip, underscoring its enduring appeal in times of crisis.

Digging deeper into the crypto sell-off, the liquidation cascade offers a window into the mechanics of this downturn. For Ethereum, a single whale’s US$106 million loss—triggered by the sale of 67,570 ETH on Maker—appears to have sparked a chain reaction, dragging prices from above US$1,800 to US$1,500 in a matter of hours. Another investor’s sale of 14,014 ETH, valued at $22 million, further amplified the panic, pushing Ethereum to levels not seen since October 2023.

These events highlight the leveraged nature of the crypto market, where large positions can magnify price swings, especially during periods of heightened uncertainty. Bitcoin, while less severely impacted, still saw its own wave of liquidations, with US$247 million wiped out as traders rushed to exit long positions.

Also Read: US tariffs vs crypto wins: An economic shift

In my humble point of view, the tariffs are acting as a double-edged sword for cryptocurrencies. On one hand, they’re stoking fears of slower growth and higher inflation—conditions that could, over time, drive adoption of decentralised assets as a hedge against traditional systems.

Trump’s own pro-crypto stance, including his March announcement of a strategic reserve featuring Bitcoin and Ethereum, lends credence to this narrative. Yet, in the immediate term, the market is behaving more like a risk proxy than a safe haven. The Fear & Greed Index, a barometer of crypto sentiment, remains mired in “fear” territory, a stark contrast to the exuberance of earlier this year.

Looking ahead, the trajectory of this trade war will be critical. Federal Reserve Chair Jerome Powell has signalled that the central bank won’t rush to cut rates in response to the tariffs, despite their potential to slow US growth and stoke inflation. This stance could exacerbate the pressure on risk assets if inflationary pressures persist without monetary relief.

For Bitcoin and Ethereum, a prolonged period of market turmoil could test key support levels—US$75,000 for Bitcoin and US$1,400 for Ethereum—before any recovery takes hold. Yet, if the tariffs weaken confidence in fiat currencies or trigger a broader shift away from dollar-centric systems, as some experts predict, cryptocurrencies could emerge stronger on the other side.

As I reflect on these developments, I’m struck by the paradox at play. Trump’s tariffs, intended to strengthen the US economy, are instead unleashing chaos across global markets, including the very crypto ecosystem he’s championed. For investors, the challenge lies in navigating this volatility—balancing the short-term pain of sell-offs against the long-term promise of digital assets. From where I stand, the story is far from over.

The coming weeks will reveal whether this is a temporary blip or the start of a deeper reckoning for cryptocurrencies and the global economy alike. One thing is certain: in this interconnected world, no market is an island, and the reverberations of “Liberation Day” will be felt for months, if not years, to come.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Join us on Instagram, Facebook, X, and LinkedIn to stay connected.

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As artificial intelligence continues its swift march into every corner of our lives, the stakes for the Southeast Asian region could not be higher. From cutting-edge financial tools to automated factories, AI’s potential to reshape industries has never been more apparent.

The AI market in the Asia-Pacific region was valued at US$50.41 billion in 2023 and is projected to reach approximately US$735 billion by 2030. But alongside these new possibilities come new risks, particularly in cybersecurity.

In 2024, Kaspersky’s Security Network detected over 5 million web threats in Singapore alone, averaging 14,000 web-based attacks per day. Meanwhile, PwC’s 2023 Global Risk Survey found that 69 per cent of organisations view themselves as highly exposed to cyber risks. Additionally, 59.6 per cent of enterprises in the Asia-Pacific region experienced ransomware attacks in 2023, underscoring the urgent need for enhanced cybersecurity measures, according to IDC.

If Asia is to harness AI’s full promise, it must act quickly to develop the right skills, policies, and collaborative networks capable of staying ahead of increasingly sophisticated cyber threats.

Recent developments in Singapore illustrate the urgency of building a robust cybersecurity framework: the Cyber Security Agency (CSA) has intensified efforts to protect critical infrastructure, deploying AI-driven threat detection and cloud-security measures that respond to attacks in real time. In Asia, businesses are doubling down on extended detection and response (XDR) solutions, AI-powered threat intelligence, and stronger endpoint protections.

AI offers a powerful defensive shield and fuels a new generation of threats: automated cyberattacks, polymorphic malware that outsmarts traditional security measures, and hyper-realistic phishing campaigns that are increasingly difficult to detect.

For organisations, the path forward involves embracing AI-driven defence strategies, focusing on zero-trust architectures, continuous behavioural anomaly detection, and automated incident responses. It also demands a workforce capable of building, managing, and interpreting these intricate tools, which means the AI revolution depends on talent.

Machine learning and deep learning skill sets, especially in  Python, TensorFlow, PyTorch, and Scikit-learn, are in high demand. AI engineers with cloud platform proficiency in AWS, Azure, and Google Cloud, now find themselves at the epicentre of the technological transformation.

Cybersecurity specialists trained in AI-driven threat detection, automated response, and risk analytics have become indispensable to organisations on the front lines.

Natural language processing (NLP) has also taken centre stage, powering everything from chatbots to generative AI systems like large language models (LLMs). In Singapore, 79 per cent of employees are now using GenAI daily, surpassing the global rate, due in part  to the national AI roadmap 2021-2025, which promotes AI use across various sectors.

With great power comes great responsibility. As AI becomes more ubiquitous, experts in ethical AI and responsible deployment must ensure that algorithms remain transparent, unbiased, and in line with local data protection laws.

From banking to healthcare, AI’s footprints are visible everywhere. Financial services rely on AI to spot fraudulent transactions and provide automated customer support through robo-advisors. Healthcare institutions are turning to predictive analytics for patient monitoring and diagnostics, while factories adopt AI to predict equipment failures before they cause expensive downtime. Online retailers leverage AI to offer personalised shopping experiences and optimise inventory management.

Also Read: AI glasses for the visually impaired are quietly powering the next economic surge

Yet, all these AI-driven applications hinge on data and securing that data remains a top priority.

Companies across Asia are increasingly turning to AI-based cybersecurity tools that can identify anomalies in real time and respond before vulnerabilities become crises. Firms such as Exclusive Networks are equipping organisations across various industries with cloud-enabled, AI-driven security services.

Some wonder if AI-generated code will render human developers obsolete. While tools like GitHub Copilot and ChatGPT can automate routine coding tasks, human creativity, problem-solving, and architectural oversight remain irreplaceable.

The future of software development belongs to those who can collaborate with AI, integrating automated tools into their workflows and focusing on higher-level system design, cybersecurity, and ethical AI considerations.

The need for secure coding practices may become even more pronounced, as AI-generated code can harbor undetected vulnerabilities. Developers in Asia who embrace continuous up-skilling in everything from cyber-risk management to data ethics will be best positioned to thrive in this new era.

Despite AI’s rapid ascent, a skills gap persists. Only 23 per cent of Southeast Asian companies are transformative in their AI adoption, indicating significant potential for growth in AI integration. However, tech professionals face limited access to specialised training programs, especially those covering AI model deployment, automation, and AI-specific security protocols.

With the complex regulatory landscape, organisations must comply with local data protection laws, including the Personal Data Protection Act (PDP Act), as they implement AI-driven solutions. Meanwhile, AI enables increasingly sophisticated attacks like deepfakes, requiring businesses and individuals alike to remain vigilant against new deceptive tactics.

Private companies can and should take the lead in up-skilling Asia’s workforce. By offering comprehensive training and certification opportunities, businesses can directly address the AI talent shortage. For example, Exclusive Networks provides cybersecurity education through the Exclusive Training Centre (ETC) and Exclusive Academy, helping professionals stay up-to-date with the latest AI-driven security tools.

Beyond training, organisations must invest in AI research and innovation. Collaborating with universities, tech institutes, and government agencies will cultivate a pipeline of skilled professionals. Equally crucial is collaboration on AI ethics and governance to ensure that new technologies remain transparent, fair, and respectful of user privacy.

Also Read: AI-powered brain health app BrainEye sets sights on Indonesia launch

Ultimately, government action is paramount to establishing a thriving AI ecosystem. Governments across Asia are actively investing in AI development, with regional AI investments projected to reach US$110 billion by 2028. Expanding educational grants, funding AI-focused research, and introducing targeted tax incentives for AI-powered enterprises will accelerate AI adoption and innovation.

Asia must also fortify its AI governance, clarifying regulations around data privacy and ensuring new AI applications do not compromise ethical standards or national security. Still, this is a challenge we cannot leave to governments alone. Technological change moves swiftly, requiring a combination of public policy, private investment, and relentless talent development to keep pace.

If Asia embraces these imperatives, fostering innovative AI research, addressing the cybersecurity skills gap, and promoting responsible AI governance, it will be well-positioned to reap the rewards of this new technological revolution.

The future is here, and it runs on AI. The question is whether Asia and its industries will harness the power of AI responsibly and securely or be left vulnerable to the very innovations meant to propel them forward.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Join us on Instagram, Facebook, X, and LinkedIn to stay connected.

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